JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 239
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #308312
Title: Bias Correction for Covariance Parameter MLEs in GLMMs
Author(s): Elizabeth Claassen*+ and Christopher Gotwalt and Walt W. Stroup
Companies: University of Nebraska-Lincoln and SAS Institute and University of Nebraska-Lincoln
Keywords: Maximum Likelihood Estimation ; Generalized Linear Mixed Models ; Firth correction ; REML

Maximum likelihood estimation in linear mixed models (LMMs) is known to produce biased estimates of covariance parameters. Restricted Maximum Likelihood (REML) is the standard method for estimation in LMMs because of its bias reduction properties. While less commonly appreciated, MLEs of covariance parameters in generalized linear mixed models (GLMMs) are similarly biased. Firth (1993) developed a bias adjustment for MLEs. Gotwalt (2012) showed REML to be a special case of the Firth adjustment for LMMs with linear covariance structures. We will show preliminary work towards developing a Firth correction for GLMMs. For certain GLMMs this could be viewed as a generalized analog to REML.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2013 program

2013 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.